samp_physig {sensiPhy}

Sensitivity Analysis Species Sampling - Phylogenetic signal

Description

Performs analyses of sensitivity to species sampling by randomly removing species and detecting the effects on phylogenetic signal estimates

Usage

samp_physig(
  trait.col,
  data,
  phy,
  n.sim = 30,
  breaks = seq(0.1, 0.5, 0.1),
  method = "K",
  track = TRUE,
  ...
)

Arguments

trait.col

The name of a column in the provided data frame with trait to be analyzed (e.g. "Body_mass").

data

Data frame containing species traits with row names matching tips in phy.

phy

A phylogeny (class 'phylo') matching data.

n.sim

The number of times to repeat species random removal for each break interval.

breaks

A vector containing the percentages of species to remove.

method

Method to compute signal: can be "K" or "lambda".

track

Print a report tracking function progress (default = TRUE)

...

Further arguments to be passed to phylosig

Details

This function randomly removes a given percentage of species (controlled by breaks) from the full data, estimates phylogenetic signal for a given trait (K or lambda) without these species using phylosig, then repeats the analysis many times (controlled by n.sim), stores the results and calculates the effect of random species removal on phylogenetic signal estimates.

Output can be visualised using sensi_plot.

Value

The function samp_phylosig returns a list with the following components:

Trait: Column name of the trait analysed

full.model.estimates: Phylogenetic signal (K or lambda) and p-value using the full dataset (without deleted species). See phylosig for details.

sensi.estimates: A data frame with all simulation estimates. Each row represents a rerun with a given number of species n.remov removed, representing n.percent of the full dataset. Columns report the calculated signal estimate (estimate), difference between reduced data signal estimate and full data signal (DF), the percentage of change in signal compared to the full data estimate (perc) and signal p-value for the reduced data estimate(pval).

sign.analysis For each break (i.e. each percentage of species removed) this reports the percentage of statistically significant (at p<0.05) phylogenetic signal over all repetitions with reduced data sets.

data: Original full dataset used in the analysis. #' @note Please be aware that dropping species may reduce power to detect significant signal and may partially be responsible for a potential effect of species removal on p-values. Please also consult standardised differences in the (summary) output.

Note

The argument "se" from phylosig is not available in this function. Use the argument "V" instead with intra_physig to indicate the name of the column containing the standard deviation or the standard error of the trait variable instead.

Author(s)

Gustavo Paterno & Gijsbert D.A. Werner

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467.

Werner, G.D.A., Cornwell, W.K., Sprent, J.I., Kattge, J. & Kiers, E.T. (2014). A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms. Nature Communications, 5, 4087.

Blomberg, S. P., T. Garland Jr., A. R. Ives (2003) Testing for phylogenetic signal in comparative data: Behavioral traits are more labile. Evolution, 57, 717-745.

Pagel, M. (1999) Inferring the historical patterns of biological evolution. Nature, 401, 877-884.

Kamilar, J. M., & Cooper, N. (2013). Phylogenetic signal in primate behaviour, ecology and life history. Philosophical Transactions of the Royal Society B: Biological Sciences, 368: 20120341.

See Also

phylosig, samp_phylm,sensi_plot

Examples

## Not run: 
data(alien)
alien.data<-alien$data
alien.phy<-alien$phy
# Logtransform data
alien.data$logMass <- log(alien.data$adultMass) 
# Run sensitivity analysis:
samp <- samp_physig(trait.col = "logMass", data = alien.data, n.sim = 30,
phy = alien.phy[[1]])
summary(samp)
sensi_plot(samp)
sensi_plot(samp, graphs = 1)
sensi_plot(samp, graphs = 2)

## End(Not run)